1. Field of the Invention
The present invention relates to event forecasting systems, and more particularly to, crime forecasting systems for enhancing crime prevention methods.
2. Background
Police departments nationwide are facing budget freezes and deep cuts, requiring them to manage their resources more effectively while still responding to public demand for crime prevention and reduction. Because of this, there has been a more recent emphasis on attempting to predict crime before it occurs so as to focus precious resources in higher crime risk areas to maximize the potential of the public safety force.
Examples of more basic crime prediction or forecasting techniques include: crime counts, pin maps depicting past crime locations, and crime hotspot maps (density estimation) as some of the proposed methods of managing and allocating police resources. However, these methods have generally proven unsatisfactory because they fail to take into account both long-term spatial variation in risk as well as short term elevation in risk following crime in a systematic way.
Thus, while crime hotspot maps attempt to quantify the contagious spread of crime following past events, they fail to assess the likelihood of future “background” events, the initial events that trigger crime clusters. Moreover, crime hotspot maps typically rely on the assumption that short term crime trends will persist into the future, while ignoring the presence of background events.
In analyzing historical crime data, it is increasingly accepted that crime spreads through local environments via a contagion-like process. An initial crime (background event) can lead to a sequence of nearby, related crimes (aftershocks), similar to the spread of an epidemic or the occurrence of aftershock sequences following earthquakes. Thus, near-repeat patterns have been established to exist in certain types of crime data, for example, property crime and gang violence, where the occurrence of an event increases the likelihood of more events in the future. For example, burglars repeatedly attack clusters of nearby targets because local vulnerabilities are well-known to the offenders. Likewise, a gang shooting may incite waves of retaliatory violence in the local set space (territory) of the rival gang. The local, contagious spread of crime leads to the formation of crime clusters in space and time. Recognizing this, hotspot policing strategies use past crime clusters to estimate where crime is likely to occur in the future. In dealing with crime data, crime hotspot maps are the most widely used tool for the quantification of future crime risk and are a key element in hotspot policing.
While hotspot policing is well known, where officers are deployed to areas that have had a high crime count over a given time interval, it is much less standard to use sophisticated computer models to assign probabilities to space time regions for the purpose of allocating police patrols. One such effort at a more advanced level of crime data analysis may be found in a software application named Crimestat. However, this software does not predict near-repeat patterns and is relatively cumbersome to use and learning curve intensive since it requires expertise in GIS and additional software such as Arc GIS.
While it has long been known that crime is unevenly distributed in space forming so-called hotspots, with the advent of widespread digital mapping in the 1990s, it quickly became apparent that crime patterns are also highly dynamic with hotspots emerging and spreading through space and dissipating as rapidly as they form. Law enforcement agencies recognized that targeting crime hotspots might represent a more effective use of their resources. However, the rapidity with which crime hotspots evolve makes efficient targeting of them difficult.
One approach, widely practiced in contemporary law enforcement today, is to target only most persistent crime hotspots. This approach comes with the drawback that it is largely static and ignores the great volume of crime occurring in shorter lived and more dynamic hotspots. The alternative is to ‘chase’ all crime as it appears. The drawback here is that law enforcement may end up chasing many single-event crimes that are not part of an emerging crime pattern. Given the drawbacks of the foregoing methods, demand for more sophisticated approaches followed.
Kernel density hotspot mapping is another currently used technique to allocate police resources (Chainey and Ratcliffe, 2005). The drawback of this method is that crime patterns in the past are assumed to persist into the future and thus patrols based upon such a model focus on observed hotspots from the past.
Prospective hotspot mapping (Bowers et al, 2004) attempts to address the drawback of the Chainey and Ratcliffe method by weighting crimes in the past with a temporally decaying kernel. This method has its own drawbacks. The estimate of risk it provides is only based upon the past several weeks of data, thus ignoring valuable information contained on the spatial distribution of risk (and thus the method is susceptible to high variance in risk estimates). Furthermore, the manner by which the parameters are selected are not through any optimization procedure and thus for a given city the kernel used is likely inaccurate.
Thus, while prospective hotspotting or prospective hotspot mapping is an existing method that has been developed to forecast near-repeat patterns for the purpose of directed patrols, this method fails to distinguish between spontaneous events (the analogy in seismology would be mainshocks or the main event) and triggered events (aftershocks). Moreover, the method does not allow for parameter estimation using a rigorous methodology such as Maximum Likelihood Estimation (MLE) and is less suitable for automation or use on a large scale.
Since the risk associated with background events comprises a substantial proportion of the total future crime risk and the failure to account for these events has important implications for crime prediction, what is needed and previously unavailable is an improved predictive policing system that provides targeted, real-time, crime prediction forecasts that may take into account both spontaneous and triggered events and that are presented in a user friendly format for patrol officers and shift commanders to better manage limited patrol resources.